Improved Monte Carlo Estimation of the Fisher Information Matrix with Independent Perturbations

2021 ◽  
Author(s):  
Xuan Wu ◽  
James C. Spall
Keyword(s):  
Monte Carlo ◽  
Fisher Information ◽  
Fisher Information Matrix ◽  
Information Matrix ◽  
Monte Carlo Estimation

Robust designs accounting for model uncertainty in longitudinal studies with binary outcomes

2019 ◽  
Vol 29 (3) ◽  
pp. 934-952
Author(s):  
Jérémy Seurat ◽  
Thu Thuy Nguyen ◽  
France Mentré
Keyword(s):  
Monte Carlo ◽  
Longitudinal Studies ◽  
Robust Design ◽  
Fisher Information ◽  
Model Uncertainty ◽  
Fisher Information Matrix ◽  
Information Matrix ◽  
Average Power ◽  
Binary Outcomes ◽  
Mixed Effect

To optimize designs for longitudinal studies analyzed by mixed-effect models with binary outcomes, the Fisher information matrix can be used. Optimal design approaches, however, require a priori knowledge of the model. We aim to propose, for the first time, a robust design approach accounting for model uncertainty in longitudinal trials with two treatment groups, assuming mixed-effect logistic models. To optimize designs given one model, we compute several optimality criteria based on Fisher information matrix evaluated by the new approach based on Monte-Carlo/Hamiltonian Monte-Carlo. We propose to use the DDS-optimality criterion, as it ensures a compromise between the precision of estimation of the parameters, and hence the Wald test power, and the overall precision of parameter estimation. To account for model uncertainty, we assume candidate models with their respective weights. We compute robust design across these models using compound DDS-optimality. Using the Fisher information matrix, we propose to predict the average power over these models. Evaluating this approach by clinical trial simulations, we show that the robust design is efficient across all models, allowing one to achieve good power of test. The proposed design strategy is a new and relevant approach to design longitudinal studies with binary outcomes, accounting for model uncertainty.

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